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Issue:Intuitionistic fuzzy logic adaptation of particle swarm optimization

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Title of paper: Intuitionistic fuzzy logic adaptation of particle swarm optimization
Author(s):
Patricia Melin
Tijuana Institute of Technology,, Tijuana BC México
pmelin@tectijuana.mx
Daniela Sánchez
Tijuana Institute of Technology,, Tijuana BC México
danielasanchez.itt@hotmail.com
Pencho Marinov
Bulgarian Academy of Sciences, Sofia, Sofia, Bulgaria
pencho@parallel.bas.bg
Presented at: 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria
Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 23, 2017, Number 2, pages 95—102
Download:  PDF (157 Kb  Kb, File info)
Abstract: In this paper a new Modular Neural Network (MNN) optimization is proposed, where a particle swarm optimization with an intuitionistic fuzzy dynamic parameter adaptation designs optimal MNNs architectures. This design consists in to find the number of hidden layers for each sub module with their respective number of neurons, learning method, error goal and the percentage of data used for the training phase. The proposed intuitionistic fuzzy adaptation seeks to avoid stagnation of error of recognition during iterations updating some PSO parameters.
Keywords: Intuitionistic fuzzy logic, Particle Swarm Optimization, Iris recognition, Human recognition.
AMS Classification: 03E72
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